The more relevant continuation of Heng Li's posts is Pall Melsted et al's work on GFA: https://github.com/GFA-spec/GFA-spec

This is pretty close to my area...

The main one is vg, which I think Benedict Paten is actively supervising, along with Richard Durbin. The main developer is Erik Garrison. I'm pretty surprised it wasn't mentioned by name: https://github.com/vgteam/vg

Actually there are new approaches to study exactly that, and they are developing rapidly, cf: https://en.wikipedia.org/wiki/Chromosome_conformation_captur... Basically, this problem, along with a long list of other applications is being attacked by deep sequencing. The way it works is that you apply a treatment to DNA that 'glues' 3D contacts in place and covers the glued segment, apply restriction enzymes to cut out what's not covered by glue, get rid of the glue, sequence all that's left, and then map the remaining sequenced fragments to the reference genome. The output is the relative tendency of different regions to come into contact with each other.

> The 3-D organization of the genome can also be analyzed via eigendecomposition of the contact matrix.

Love it when linear algebra pops up in unexpected places!

It could be that the standard for moonshots changed with the formation of Alphabet. That's the impression I got from yesterday's press coverage.

The mention of groups of robots learning together by exposing themselves to the real world and sharing lessons is very interesting to consider. I wonder if this is inspired by ants searching for food and leaving scent trails. Are there success stories of this approach in robotics already?

Sounds like an accurate description of a ponzi scheme.

I believe the phrase is "Fake it 'til you Make it" - whether this is a legitimate approach tends to only be obvious in retrospect.

It is troubling if this is the mantra of a manufacturer of medical equipment, however.

I don't think their intent is to replace existing database servers. My impression is that they have copied the data onto their servers to make analysis on their cloud service more attractive. For example, the fact that the 1000 Genomes data is there saves researchers from having to go to the trouble of uploading it themselves.

Having worked with them a bit my impression is that at a high level their intent is to get people to spend money using google cloud services. With that in mind it is not in their interest to annoy half of the field by playing shenanigans with things like data ownership

> I don't think their intent is to replace existing database servers.

We thought the same with Freebase and the Knowledge Graph. It will happen again.

same here, had to watch some of those lectures at 2x speed

Outside of DBs, indexes have shown themselves to be extremely useful in string problems in bioinformatics (my area of research). The modern workhorses of this trend are the Burrows Wheeler transform + FM index (together) and Bloom filters (also mentioned in the link). These have been applied to sequence alignment, de novo assembly of genomes, and compression of sequences. I posit the same bag of tricks can be applied in the NLP/machine learning settings, but I know less about how commonly they have been.

There's a good set of notes touching on this stuff here: http://www.langmead-lab.org/teaching-materials/

Twitter is actually becoming quite popular in science. It's a great tool to hear of new papers, find out about new findings reported in conferences you can't attend, and communicate in a public forum. Don't think you would have had much of that in MySpace...